Our work is also influenced by Neural Network with Random Weights (NNRW), a single-hidden-layer neural network with random weights from the input layer to the hidden layer. The weights between the input and the hidden layers are randomly generated in a suitable range and kept fixed during the training process. Compared with conventional learning methods with global tuning, such as deep learning with the back propagation, NNRW mitigates problems related to slow learning convergence and local optimality traps due to highly non-convex optimization.
NNRW can achieve a much faster training speed with acceptable accuracy. Furthermore, NNRW is easy to implement, and is theoretically capable of universal approximation [1]}, [2]}, [3]}. Although many studies of NNRWs have been conducted under several names, such as Extreme Learning Machine (ELM) [4]}, Random Vector-Functional Link (RVFL) networks [5]}, and Schmidt's method [6]}, the underlying ideas and training mechanism of the three methods are very similar, and so we briefly introduce the formulation of ELM model as follows.
